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In the field of acoustic suspension or levitation of droplets against gravity, the application of Gorkov's acoustic radiation force for small particles (within the Rayleigh limit) or its extensions to larger ones (beyond the Rayleigh limit) is limited to predicting the suspension position of the droplet. Since this approach treats the droplet as a rigid particle, it fails to capture the fluid dynamics of the droplet and is also unsuitable for studying interfacial phenomena such as droplet deformation, splitting, or coalescence. In this work, we employ our recently developed acoustic body force in Eulerian form, which models the droplet as a fluid, to theoretically investigate the suspension dynamics of droplet subjected to standing waves through the interaction between acoustic, interfacial, and gravitational forces. Our theory predicts that when interfacial forces are dominant, the presence of positive and negative acoustic force regions within droplets exceeding the Rayleigh limit reduces the net acoustic force counteracting gravity. As a result, the suspension dynamics become highly dependent on droplet size, in contrast to droplets within the Rayleigh limit, where the dynamics remain size-independent. Thus, beyond the Rayleigh limit, as the droplet size to wavelength ratio increases, the critical acoustic energy density ($E_{cr}$) required to suspend the droplet initially rises sharply, which agrees with recent experimental results. After $E_{cr}$ reaches a local maximum at $d/\lambda \approx 0.65$, it exhibits a pattern of alternating decreases and increases, with each successive peak surpassing the previous one. Remarkably, our study reveals a size-dependent shifting of the suspension position between nodes and antinodes for droplets beyond the Rayleigh limit, whereas droplets within this limit maintain a consistent suspension position regardless of size.

Network performance modeling presents important challenges in modern computer networks due to increasing complexity, scale, and diverse traffic patterns. While traditional approaches like queuing theory and packet-level simulation have served as foundational tools, they face limitations in modeling complex traffic behaviors and scaling to large networks. This project presents an extended implementation of RouteNet-Fermi, a Graph Neural Network (GNN) architecture designed for network performance prediction, with additional recurrent neural network variants. We improve the the original architecture by implementing Long Short-Term Memory (LSTM) cells and Recurrent Neural Network (RNN) cells alongside the existing Gated Recurrent Unit (GRU) cells implementation. This work contributes to the understanding of recurrent neural architectures in GNN-based network modeling and provides a flexible framework for future experimentation with different cell types.

Large Language Models (LLMs) have shown exceptional performance across various Data-to-Text Generation (DTG) tasks. However, generating factually consistent text in DTG remains challenging for LLMs. Despite this, in-depth evaluations of LLM factual consistency for DTG remain missing in the current literature. This paper addresses this gap by providing an extensive evaluation of factual consistency in LLMs for DTG. Our evaluation covers five widely used DTG datasets (E2E, ViGGo, WikiTableText, DART, and WebNLG) and five prominent LLM families (T5, BART, OPT, BLOOM, and Llama 2). To ensure a thorough evaluation of factual consistency, we use four state-of-the-art automatic metrics and include essential human assessments. Our extensive evaluations reveals three key findings regarding factual consistency in LLMs for DTG. First, Llama 2 often excels in generating factually consistent text, although smaller models like T5 and BART can achieve strong factual consistency on larger, lexically less-diverse datasets. Second, the average rate of change (AROC) indicates that increasing model size (number of model trainable parameters) generally enhances factual consistency of LLMs in DTG. Third, we observe that source-reference divergence (i.e., when the reference text diverges semantically from the source) typically reduces the factual consistency of LLMs in DTG., Comment: 15 pages

We investigate the role of both magnetic field and chemical potential on the emergence of chaotic dynamics in the QCD confining string from the holographic principle. An earlier developed bottom-up model of Einstein-Maxwell-dilaton gravity, which mimics QCD features quite well, is used. The qualitative information about the chaos is obtained using the Poincar\'{e} sections and Lyapunov exponents. Our results depend quite strongly on the frame we consider in the analysis. In the string frame, the chemical potential and the magnetic field suppress the chaotic dynamics in both parallel and perpendicular orientations of the string with respect to the magnetic field. Meanwhile, in the Einstein frame, the magnetic field suppresses/enhances the chaotic dynamics when the string is orientated perpendicular/parallel to the magnetic field, while the chemical potential enhances the chaotic dynamics for both orientations. We further analyse the MSS bound in the parameter space of the model and find it to be always satisfied in both frames., Comment: 31 Pages, many figures, comments are welcome

We discuss the classical and quantum chaos of closed strings on a recently constructed charged confining holographic background. The confining background corresponds to the charged soliton, which is a solution of minimal $d=5$ gauged supergravity. The solution has a compact spacelike direction with a Wilson line on a circle and asymptotes to $AdS_5$ with a planar boundary. For the classical case, we analyze the chaos using the power spectrum, Poincar\'{e} sections, and Lyapunov exponents, finding that both energy and charge play constructive effects on enhancing the chaotic nature of the system. We similarly analyze quantum chaos using the distribution of the spectrum's level-spacing and out-of-time-ordered correlators and thoroughly investigate the effects of charge and energy. A gradual transition from a chaotic to an integrable regime is obtained as the energy and charge increase from lower to higher values, with charge playing a subdominant role., Comment: 28 pages, many figures. Comments are welcome

Consumer lighting plays a significant role in the development of smart cities and smart villages. With the advancement of (IoT) technology, smart lighting solutions have become more prevalent in residential areas as well. These solutions provide consumers with increased energy efficiency, added convenience, and improved security. On the other hand, the growing number of IoT devices has become a global concern due to the carbon footprint and carbon emissions associated with these devices. The overuse of batteries increases maintenance and cost to IoT devices and simultaneously possesses adverse environmental effects, ultimately exacerbating the pace of climate change. Therefore, in tandom with the principles of Industry 4.0, it has become crucial for manufacturing and research industries to prioritize sustainable measures adhering to smart energy as a prevention to the negative impacts. Consequently, it has undoubtedly garnered global interest from scientists, researchers, and industrialists to integrate state-of-the-art technologies in order to solve the current issues in consumer light management systems making it a complete sustainable, and smart solution for consumer lighting application. This manuscript provides a thorough investigation of various methods as well as techniques to design a state-of-the-art IoT-enabled consumer light management system. It critically reviews the existing works done in consumer light management systems, emphasizing the significant limitations and the need for sustainability. The top-down approach of developing sustainable computing frameworks for IoT-enabled consumer light management has been reviewed based on the multidisciplinary technologies involved and state-of-the-art works in the respective domains. Lastly, this article concludes by highlighting possible avenues for future research., Comment: 39 pages, 21 Figures excluding authors' biography

We present experimental evidence of orbital Hall torque and unidirectional magnetoresistance (UMR), arising from the orbital Hall effect generated by the transverse flow of orbital angular momentum in light metals under an applied electric current. Through second-harmonic measurements, we investigate orbital Hall torque and UMR in bilayer devices composed of ferromagnetic materials (FM), such as Ni and NiFe, paired with light metals (LM), such as Ti and Nb. Our results demonstrate that LM/Ni bilayers exhibit enhanced damping-like torque and unidirectional magnetoresistance (UMR) compared to LM/NiFe bilayers. This enhancement suggests that angular momentum is generated via the orbital Hall effect within the light metal, where it undergoes orbital-to-spin conversion within the Ni ferromagnet, ultimately transferring to the magnetization of the ferromagnetic layer. Torque and UMR are also absent in single-layer devices, highlighting the necessity of the bilayer structure for orbital current generation.

In this paper, several significant upper bounds for the numerical radius and $a$-numerical radius of an element in a $\mathcal{C}^*$-algebra are obtained using Orlicz functions. Many well-known results are obtained from our findings, depending on specific choices of Orlicz functions.

The demand for GaN Nanowires (NWs)-based optoelectronic devices has rapidly increased over the past few years due to its superior crystalline quality compare to their planar counterparts. However, NWs-based devices face significant challenges because of number of surface states, basal plane stacking faults and coalescence related defect states. While the origins of most of the defect states have been identified and mitigated using well-established methods, the origins of few defect states remain unknown, and thus their suppression methods have yet to be explored. One such defect state is the 3.45 eV luminescence peak, known as the UX-band. In this report, we have investigated the origin of this peak in NWs grown on a sapphire substrate by using Plasma Assisted Molecular Beam Epitaxy (PAMBE) tool. We have found that the defect states, generated due to oxygen incorporation, especially when the oxygen atoms substitute the Ga atoms, are the primary cause of this band. Actually the excitons are localized at this defect center and the radiative recombination of localized excitons gives the characteristic UX-band. By protecting the NWs from oxygen incorporation through AlN encapsulation process, we have completely suppressed the 3.45 eV peak and proved further that the peak is caused by oxygen induced defect states. Thus we have addressed an issue that persisted over the last three decades, potentially paving the way for efficiency improvements in optoelectronic devices.

We describe observations of low energy excess (LEE) events (background events observed in all light dark matter direct detection calorimeters) and noise in a two-channel silicon athermal phonon detector with 375 meV baseline energy resolution. We measure two distinct LEE populations: ``shared'' multichannel events with a pulse shape consistent with athermal phonon events, and sub-eV events which couple nearly exclusively to a single channel with a significantly faster pulse shape. These ``singles'' are consistent with events occurring within the aluminum athermal phonon collection fins. Similarly, our measured detector noise is higher than the theoretical expectation. Measured noise can be split into an uncorrelated component, consistent with shot noise from small energy depositions within the athermal phonon sensor itself, and a correlated component, consistent with shot noise from energy depositions within the silicon crystal's phonon system., Comment: 6 pages, 5 figures

We present the results of a suite of numerical simulations to study partial tidal disruption events (TDEs) of white dwarfs (WDs) in off-equatorial planes in intermediate mass spinning (Kerr) black hole backgrounds. We carry out this analysis for both parabolic and eccentric WD orbits and also take into account possible initial WD spins. Our objective here is to quantify the differences in variables like the mass of the self-bound core, the peak fallback rate of debris and gravitational wave signature in off-equatorial orbits compared to equatorial ones. The analysis is carried out using a hybrid numerical scheme, one which involves integrating the exact Kerr geodesics while adopting a Newtonian formalism for the stellar fluid dynamics, justified by our choice of simulation parameters. We find that the physics of TDEs in off-equatorial orbits present several interesting and novel features due to black hole spin, which in some cases enhances when coupled with the rotation of the WD. However, numerical values of observable quantities in TDEs involving off-equatorial orbits cannot possibly distinguish between such orbits from equatorial ones. We further comment on the genericness of our results and argue that these should extend to a general TDE scenario involving a spinning BH., Comment: 15 Pages

We describe design studies for a pulsed quasi-monoenergetic 2-keV neutron source for calibration of sub-keV nuclear recoils. Such a calibration is required for detectors sensitive to sub-GeV dark matter and also the coherent elastic scattering of reactor neutrinos. In our design, neutrons from a commercial deuterium-tritium generator are moderated to the keV scale and then filtered to the monoenergetic spectrum using a feature in the neutron cross section of scandium. In this approach, unmoderated high-energy neutrons form a challenging background, along with gammas from neutron capture in the moderator materials. We describe the optimization of the moderator+filter and shielding geometry, and find a geometry that in simulation achieves both the target neutron flux at 2 keV and subdominant rates of background interactions. Lastly, we describe a future path to lower-energy (few eV scale) calibrations using time-of-flight and sub-keV neutrons., Comment: 10 pages, 7 figures

We explore the holographic time-like entanglement entropy (TEE) in the boundary theory of three-dimensional Lifshitz spacetime. There have been various holographic proposals for TEE in recent years and we test those proposals in the Lifshitz background. We obtain the analytic result for TEE in each proposal, compare the results, and analyze how the anisotropic scaling affects the TEE. We find that different holographic proposals give the same result for TEE in the Lifshitz background. Our analysis further suggests that the TEE of the Lifshitz system contains real and imaginary parts, both of which depend on the anisotropic parameter., Comment: 18 pages, 4 figures, discussion expanded, references added, typos corrected, published version. JHEP

This paper tackles the challenge of building robust and generalizable bias mitigation models for language. Recognizing the limitations of existing datasets, we introduce ANUBIS, a novel dataset with 1507 carefully curated sentence pairs encompassing nine social bias categories. We evaluate state-of-the-art models like T5, utilizing Supervised Fine-Tuning (SFT), Reinforcement Learning (PPO, DPO), and In-Context Learning (ICL) for effective bias mitigation. Our analysis focuses on multi-class social bias reduction, cross-dataset generalizability, and environmental impact of the trained models. ANUBIS and our findings offer valuable resources for building more equitable AI systems and contribute to the development of responsible and unbiased technologies with broad societal impact., Comment: 17 pages, 5 Figures

Surface-gated quantum dots (QDs) in semiconductor heterostructures represent a highly attractive platform for quantum computation and simulation. However, in this implementation, the barriers through which the QD is tunnel-coupled to source and drain reservoirs (or neighboring QDs) are usually non-rigid, and capacitively influenced by the plunger gate voltage (VP). In transport spectroscopy measurements, this leads to complete suppression of current and lifting of Coulomb blockade, for large negative and positive values of VP, respectively. Consequently, the charge-occupancy of the QD can be tuned over a rather small range of VP. By dynamically tuning the tunnel barriers to compensate for the capacitive effect of VP, here we demonstrate a protocol which allows the Coulomb blockade to be preserved over a remarkably large span of charge-occupancies, as demonstrated by clean Coulomb diamonds and well-resolved excited state features. The protocol will be highly beneficial for automated tuning and identification of the gatevoltage-space for optimal operation of QDs, in large arrays required for a scalable spin quantum computing architecture., Comment: 5 pages, 5 figures, 1 table

The possible existence of axions in the universe introduces the intriguing possibility of photon-axion conversion in strong magnetic fields, particularly near compact objects like supermassive black holes or even naked singularity. In this study, we investigate the conversion of photons into axions in the vicinity of a Janis-Newman-Winicour (JNW) spacetime, a well-known naked singularity solution. Our analysis reveals that photons can efficiently convert into axions with masses less than $100 \rm \ neV$. We calculate the conversion probability and find that it is significantly influenced by the characteristic parameter of the JNW spacetime. The potential observational signatures of this conversion, would be the dimming of photon ring in the X-ray and gamma-ray spectrum. Our findings suggest that compact objects like M87* could be prime candidates for detecting photon-axion conversion effects, provided future advances in high-resolution observations., Comment: 18 figures, 44 pages

We analyze the steady viscoelastic fluid flow in slowly varying contracting channels of arbitrary shape and present a theory based on the lubrication approximation for calculating the flow rate-pressure drop relation at low and high Deborah ($De$) numbers. Unlike most prior theoretical studies leveraging the Oldroyd-B model, we describe the fluid viscoelasticity using a FENE-CR model and examine how the polymer chains' finite extensibility impacts the pressure drop. We employ the low-Deborah-number lubrication analysis to provide analytical expressions for the pressure drop up to $O(De^4)$. We further consider the ultra-dilute limit and exploit a one-way coupling between the parabolic velocity and elastic stresses to calculate the pressure drop of the FENE-CR fluid for arbitrary values of the Deborah number. Such an approach allows us to elucidate elastic stress contributions governing the pressure drop variations and the effect of finite extensibility for all $De$. We validate our theoretical predictions with two-dimensional numerical simulations and find excellent agreement. We show that, at low Deborah numbers, the pressure drop of the FENE-CR fluid monotonically decreases with $De$, similar to the previous results for the Oldroyd-B and FENE-P fluids. However, at high Deborah numbers, in contrast to a linear decrease for the Oldroyd-B fluid, the pressure drop of the FENE-CR fluid exhibits a non-monotonic variation due to finite extensibility, first decreasing and then increasing with $De$. Nevertheless, even at sufficiently high Deborah numbers, the pressure drop of the FENE-CR fluid in the ultra-dilute and lubrication limits is lower than the corresponding Newtonian pressure drop.

In this work, we investigate the Krylov complexity in quantum optical systems subject to time--dependent classical external fields. We focus on various interacting quantum optical models, including a collection of two--level atoms, photonic systems and the quenched oscillator. These models have Hamiltonians which are linear in the generators of $SU(2)$, $H(1)$ (Heisenberg--Weyl) and $SU(1,1)$ group symmetries allowing for a straightforward identification of the Krylov basis. We analyze the behaviour of complexity for these systems in different regimes of the driven field, focusing primarily on resonances. This is achieved via the Gauss decomposition of the unitary evolution operators for the group symmetries. Additionally, we also investigate the Krylov complexity in a three--level $SU(3)$ atomic system using the Lanczos algorithm, revealing the underlying complexity dynamics. Throughout we have exploited the the relevant group structures to simplify our explorations., Comment: 23 pages, 15 figures, 4 appendices

In medical image classification, supervised learning is challenging due to the lack of labeled medical images. Contrary to the traditional \textit{modus operandi} of pre-training followed by fine-tuning, this work leverages the visual-textual alignment within Vision-Language models (\texttt{VLMs}) to facilitate the unsupervised learning. Specifically, we propose \underline{Med}ical \underline{Un}supervised \underline{A}daptation (\texttt{MedUnA}), constituting two-stage training: Adapter Pre-training, and Unsupervised Learning. In the first stage, we use descriptions generated by a Large Language Model (\texttt{LLM}) corresponding to class labels, which are passed through the text encoder \texttt{BioBERT}. The resulting text embeddings are then aligned with the class labels by training a lightweight \texttt{adapter}. We choose \texttt{\texttt{LLMs}} because of their capability to generate detailed, contextually relevant descriptions to obtain enhanced text embeddings. In the second stage, the trained \texttt{adapter} is integrated with the visual encoder of \texttt{MedCLIP}. This stage employs a contrastive entropy-based loss and prompt tuning to align visual embeddings. We incorporate self-entropy minimization into the overall training objective to ensure more confident embeddings, which are crucial for effective unsupervised learning and alignment. We evaluate the performance of \texttt{MedUnA} on three different kinds of data modalities - chest X-rays, eye fundus and skin lesion images. The results demonstrate significant accuracy gain on average compared to the baselines across different datasets, highlighting the efficacy of our approach.

We generalize the potential reconstruction method to set up a dynamical Einstein-Born-Infeld-dilaton model, which we then use to study holographic quarkonium melting in an external magnetic field. The non-linear nature of the model allows to couple the magnetic field to the quarkonium inner structure without having to introduce back-reacting charged flavour degrees of freedom. The magnetic field dependent melting temperature is computed from the spectral functions and suggests a switch from inverse magnetic to magnetic catalysis when the magnetic field increases. We also discuss the differences due to the anisotropy brought in by the external field., Comment: 34 pages, 17 figures, a minor change in the abstract, the earlier version had an error in the computation of the melting temperature which is corrected now, references added, published version

The presence of Dark matter (DM) within a neutron star (NS) can substantially influence the macroscopic properties. It is commonly assumed that the pressure inside an NS is isotropic, but in reality, pressure is locally anisotropic. This study explores the properties of anisotropic NS with a subfraction of DM (isotropic) trapped inside. Implementing a two-fluid formalism with three Equations of State (EOS): AP3 (a realistic nucleon-nucleon interaction model), BSk22 (modeling atomic nuclei and neutron-matter), and MPA1 (considering relativistic effects in nuclear interactions). The properties of NS, such as mass ($M$), radius ($R$), and dimensionless tidal deformability ($\Lambda$), for various DM-anisotropic configurations, have been rigorously tested against observational constraints. These constraints include data from the binary NS merger GW170817, NICER x-ray measurements, and pulsar mass-radius observations. We observe that with increasing DM subfraction, higher anisotropies could also satisfy the observational constraints. Furthermore, increasing the coupling ($g$) between DM and its mediator leads to the formation of a core-halo structure, with a DM halo surrounding the baryonic matter (BM). Specifically, for coupling values of $g = 10^{-4}$, $10^{-3.7}$, and $10^{-3.5}$, we observe that the maximum radius ($R_{max}$) decreases with increasing anisotropy, which contrasts with the behavior at $g = 10^{-5}$ and in scenarios with no DM. Our analysis indicates that binary pulsar systems could potentially constrain the extent of admixed anisotropic NS or, more optimistically, provide evidence for the existence of DM-admixed anisotropic NS., Comment: 29 pages, 12 figures, 6 tables

Experimental searches for axions or dark photons that couple to the standard model photon require photosensors with low noise, broadband sensitivity, and near zero backgrounds. Here, we introduce an experimental architecture, in which a small photon sensor, in our case a Transition Edge Sensor (TES) with a photon energy resolution $\sigma_\gamma = 368.4 \pm 0.4$ meV, is colocated on the same substrate as a large high sensitivity athermal phonon sensor (APS) with a phonon energy resolution $\sigma_\mathrm{phonon} = 701 \pm 2$ meV. We show that single 3.061 eV photons absorbed in the photon-sensing TES deposit $\sim$35\% of their energy in the electronic system of the TES, while $\sim$26\% of the photon energy leaks out of the photon-sensing TES during the downconversion process and becomes absorbed by the APS. Backgrounds, which we associate with the broadly observed ``low energy excess'' (LEE), are observed to be largely coupled to either the TES (``singles'' LEE), or phonon system, (``shared'' LEE). At high energies, these backgrounds can be efficiently discriminated from TES photon absorption events, while at low energies, their misidentification as photon events is well modeled. With significant sensitivity improvements to both the TES and APS, this coincidence technique could be used to suppress backgrounds in bosonic dark matter searches down to energies near the superconducting bandgap of the sensor., Comment: 5 pages, 5 figures

We propose a novel and comprehensive particle physics framework that addresses multiple cosmological tensions observed in recent measurements of the Hubble parameter, $S_8$, and Lyman-$\alpha$ forest data. Our model, termed `{\bf SIDR$+\boldsymbol{z_t}$}' (Self Interacting Dark Radiation with transition redshift), is based on an inelastic dark matter (IDM) scenario coupled with dark radiation, governed by a $U(1)_D$ gauge symmetry. This framework naturally incorporates cold dark matter (DM), strongly interacting dark radiation (SIDR), and the interactions between these components. The fluid-like behavior of the dark radiation component which originates from the self-quartic coupling of the $U(1)_D$ breaking scalar can suppress the free-streaming effects. Simultaneously, the interacting DM-DR system can attenuate the matter power spectrum at small scales. The inelastic nature of DM provides a distinct temperature dependence for the DM-DR interaction rate determined by the mass-splitting between the inelastic dark fermions which is crucial for resolving the Ly-$\alpha$ discrepancies. We present a cosmologically consistent analysis of the model by solving the relevant Boltzmann equations to obtain the energy density and number density evolution of different species of the model. The DR undergoes two ``steps" of increased energy density when the heavier dark species freeze out and become non-relativistic, transferring their entropy to the dark radiation and enhancing $\Delta N_{\rm eff}$. The analysis showcases the model's potential to uphold the Big Bang Nucleosynthesis (BBN) prediction of $\Delta N_{\rm eff}$ but dominantly producing additional contributions prior to recombination, while simultaneously achieving correct relic density of DM though an hybrid of freeze-in and non-thermal production., Comment: 27 pages, 7 captioned figures, Updated to match published version

Recent Odessa disturbance events have brought attention to the challenges associated with the interaction between Inverter-Based Resources (IBRs) and the transmission and distribution system. The NERC event diagnosis report has highlighted several issues, emphasizing the need for continuous performance monitoring of these IBRs by system operators. Key areas of concern include the mismatch of control and protection performance of IBRs between the original equipment manufacturer (OEM)-provided models and field measurements. The inability to replicate the realistic response can result in incorrect reliability and resilience studies. In this paper, we developed an approach on how to emulate the behavior of an IBR using measurement data obtained for system operators to utilize in real-time and long-term planning. Two experiments are conducted in the phasor domain and electromagnetic transients (EMT) domain to emulate the behavior for grid forming and grid following inverters under various operating conditions and the effectiveness of the proposed model is demonstrated in terms of accuracy and ease of utilizing user-defined models (UDMs)., Comment: accepted for presentation at The 2024 Annual Conference of the IEEE Industrial Electronics Society (IECON)

Null results for WIMP dark matter have led to increased interest in exploring other dark matter candidates, such as Axions and Axion-Like Particles (ALPs), which also helps in answering the strong CP problem. This experiment achieved a sub-100 DRU (differential-rate-unit, expressed in counts/keV/kg/day) background in the MeV region of interest by employing a combination of active and passive veto techniques. Such a low background facilitates the search for ALPs with axion-photon coupling $g_{a\gamma \gamma} > 10^{-6}$ and axion-electron coupling $10^{-8}< g_{aee} < 10^{-4}$ in the 1 keV to 10 MeV mass range. This indicates that the experiment has the capability to constrain the unexplored cosmological triangle in the ALP-photon parameter space for ALPs in the MeV mass range., Comment: 8 pages, 18 figures

We consider the dimensional reduction to two dimensions of certain gravitational theories in $D \geq 4$ dimensions at the two-derivative level. It is known that the resulting field equations describe an integrable system in two dimensions which can also be obtained by a dimensional reduction of the self-dual Yang-Mills equations in four dimensions. We use this relation to construct a single copy prescription for classes of gravitational solutions in Weyl-Lewis-Papapetrou coordinates. In contrast with previous proposals, we find that the gauge group of the Yang-Mills single copy carries non-trivial information about the gravitational solution. We illustrate our single copy prescription with various examples that include the extremal Reissner-Nordstrom solution, the Kaluza-Klein rotating attractor solution, the Einstein-Rosen wave solution and the self-dual Kleinian Taub-NUT solution., Comment: 54 pages; v2: references added

Data-to-text (D2T) generation aims to generate human-readable text from semi-structured data, such as tables and graphs. The recent success of D2T is largely attributed to advancements in LLMs. Despite the success of LLMs, no research has been conducted to illustrate the impact of model size on the performance of fine-tuned LLMs for D2T tasks. D2T model performance is typically assessed based on three key qualities: \textit{readability} (indicates fluency and coherence), \textit{informativeness} (measures content similarity), and \textit{faithfulness} (assesses consistency of factual information). It is currently uncertain whether increasing the size of LLMs effectively improves performance in D2T tasks across these three qualities. The objective of this study is to investigate the performance of fine-tuned LLMs in D2T tasks in terms of model size. Through extensive comparative analysis, we aim to elucidate both the advantages and limitations of scaling model sizes across five widely used D2T datasets (E2E, ViGGo, WikiTableText, DART, and WebNLG) and twelve state-of-the-art LLMs with varying sizes from five different LLM families (T5, BART, OPT, BLOOM, and Llama 2). To comprehensively cover all the three essential qualities of D2T models, we incorporate six widely recognized automatic metrics -- \textsc{BLEU}, \textsc{METEOR}, \textsc{BERTScore}, \textsc{MoverScore}, \textsc{Parent}, and \textsc{BARTScore}. We also provide an in-depth analysis of LLM performance concerning model size in the presence of source-reference divergence, a critical aspect of D2T tasks. Our investigation reveals that increasing LLM size enhances \textit{readability} and \textit{informativeness} in D2T tasks, but larger (in terms of size) LLMs may sacrifice \textit{faithfulness}. Moreover, small-sized LLMs show more resilience than larger ones when source-reference divergence is present., Comment: 30 pages

This article presents constraints on dark-matter-electron interactions obtained from the first underground data-taking campaign with multiple SuperCDMS HVeV detectors operated in the same housing. An exposure of 7.63 g-days is used to set upper limits on the dark-matter-electron scattering cross section for dark matter masses between 0.5 and 1000 MeV/$c^2$, as well as upper limits on dark photon kinetic mixing and axion-like particle axioelectric coupling for masses between 1.2 and 23.3 eV/$c^2$. Compared to an earlier HVeV search, sensitivity was improved as a result of an increased overburden of 225 meters of water equivalent, an anticoincidence event selection, and better pile-up rejection. In the case of dark-matter-electron scattering via a heavy mediator, an improvement by up to a factor of 25 in cross-section sensitivity was achieved., Comment: 7 pages + title and references, 4 figures, and 1 table

Audio analysis is useful in many application scenarios. The state-of-the-art audio analysis approaches assume the data distribution at training and deployment time will be the same. However, due to various real-life challenges, the data may encounter drift in its distribution or can encounter new classes in the late future. Thus, a one-time trained model might not perform adequately. Continual learning (CL) approaches are devised to handle such changes in data distribution. There have been a few attempts to use CL approaches for audio analysis. Yet, there is a lack of a systematic evaluation framework. In this paper, we create a comprehensive CL dataset and characterize CL approaches for audio-based monitoring tasks. We have investigated the following CL and non-CL approaches: EWC, LwF, SI, GEM, A-GEM, GDumb, Replay, Naive, Cumulative, and Joint training. The study is very beneficial for researchers and practitioners working in the area of audio analysis for developing adaptive models. We observed that Replay achieved better results than other methods in the DCASE challenge data. It achieved an accuracy of 70.12% for the domain incremental scenario and an accuracy of 96.98% for the class incremental scenario.

We propose a new realisation of light Dirac neutrino mass and dark matter (DM) within the framework of a non-Abelian discrete flavour symmetry based on $A_4$ group. In addition to $A_4$, we also consider a $Z_2$ and an unbroken global lepton number symmetry $U(1)_L$ to keep unwanted terms away while guaranteeing the Dirac nature of light neutrinos. The field content, their transformations and flavon vacuum alignments are chosen in such a way that the type-I Dirac seesaw generates only one light Dirac neutrino mass while the other two masses arise from scotogenic contributions at one-loop. This leads to the Dirac scoto-seesaw framework, a generalisation of the widely studied scoto-seesaw model to Dirac neutrinos. The symmetry breaking of $A_4$ leaves a remnant $\mathcal{Z}_2$ symmetry responsible for stabilising DM. Dirac nature of light neutrinos introduces additional relativistic degrees of freedom $\Delta N_{\rm eff}$ within reach of cosmic microwave background experiments., Comment: version 2: Added references, corrected typos and improved discussions; 36 pages, 11 captioned figures

This paper extends the concept of de Branges matrices to any finite $m\times m$ order where $m=2n$. We shall discuss these matrices along with the theory of de Branges spaces of $\mathbb{C}^n$-valued entire functions and their associated functions. A parametrization of these matrices is obtained using the Smirnov maximum principle for matrix valued functions. Additionally, a factorization of matrix valued meromorphic functions is discussed., Comment: 19 Pages

Automatic speech recognition (ASR) systems often falter while processing stuttering-related disfluencies -- such as involuntary blocks and word repetitions -- yielding inaccurate transcripts. A critical barrier to progress is the scarcity of large, annotated disfluent speech datasets. Therefore, we present an inclusive ASR design approach, leveraging large-scale self-supervised learning on standard speech followed by targeted fine-tuning and data augmentation on a smaller, curated dataset of disfluent speech. Our data augmentation technique enriches training datasets with various disfluencies, enhancing ASR processing of these speech patterns. Results show that fine-tuning wav2vec 2.0 with even a relatively small, labeled dataset, alongside data augmentation, can significantly reduce word error rates for disfluent speech. Our approach not only advances ASR inclusivity for people who stutter, but also paves the way for ASRs that can accommodate wider speech variations., Comment: Included in 2024 Proceedings of INTERSPEECH

In this work, we demonstrate that the modulation of carrier density can alter the superconducting transition temperature by up to $204$ mK in epitaxial Indium Nitride on Gallium Nitride, accounting for the $10$% of the transition temperature in ungated conditions. Our samples are likely free from strong localization effects and significant granularity, as indicated by $( k_{f l} \gg 1 )$, suggesting that the primary determinant of the transition temperature in InN is carrier density, rather than disorder scattering. The observed behavior is consistent with BCS s-wave superconductivity, corroborated by the superconducting parameters we measured. Furthermore, we observed a $60$% bipolar suppression of the supercurrent in our experiments., Comment: 6 pages, 5 figures, supplementary material attached. Comments are welcome

We propose a Bayesian inference framework to predict the merger history of LIGO-Virgo binary black holes (BHs), whose binary components may have undergone hierarchical mergers in the past. The framework relies on numerical relativity predictions for the mass, spin, and kick velocity of the remnant BHs. This proposed framework computes the masses, spins, and kicks imparted to the remnant of the parent binaries, given the initial masses and spin magnitudes of the binary constituents. We validate our approach by performing an ``injection study'' based on a constructed sequence of hierarchically formed binaries. Noise is added to the final binary in the sequence, and the parameters of the `parent' and `grandparent' binaries in the merger chain are then reconstructed. This method is then applied to three GWTC-3 events: GW190521, GW200220_061928, and GW190426_190642. These events were selected because at least one of the binary companions lies in the putative pair-instability supernova mass gap, in which stellar processes alone cannot produce BHs. Hierarchical mergers offer a natural explanation for the formation of BHs in the pair-instability mass gap. We use the backward evolution framework to predict the parameters of the parents of the primary companion of these three binaries. For instance, the parent binary of GW190521 has masses $72_{-22}^{+32}M_{\odot}$ and $31_{-23}^{+24}M_{\odot}$ within the 90% credible interval. Astrophysical environments with escape speeds $\geq100{\rm \, km \, s^{-1}}$ are preferred sites to host these events. Our approach can be readily applied to future high-mass gravitational wave events to predict their formation history under the hierarchical merger assumption., Comment: 21 pages, 8 figures, 5 tables

Labeling errors in datasets are common, if not systematic, in practice. They naturally arise in a variety of contexts-human labeling, noisy labeling, and weak labeling (i.e., image classification), for example. This presents a persistent and pervasive stress on machine learning practice. In particular, neural network (NN) architectures can withstand minor amounts of dataset imperfection with traditional countermeasures such as regularization, data augmentation, and batch normalization. However, major dataset imperfections often prove insurmountable. We propose and study the implementation of Rockafellian Relaxation (RR), a new loss reweighting, architecture-independent methodology, for neural network training. Experiments indicate RR can enhance standard neural network methods to achieve robust performance across classification tasks in computer vision and natural language processing (sentiment analysis). We find that RR can mitigate the effects of dataset corruption due to both (heavy) labeling error and/or adversarial perturbation, demonstrating effectiveness across a variety of data domains and machine learning tasks.

This paper discusses an abstract Kramer sampling theorem for functions within a reproducing kernel Hilbert space (RKHS) of vector valued holomorphic functions. Additionally, we extend the concept of quasi Lagrange-type interpolation for functions within a RKHS of vector valued entire functions. The dependence of having quasi Lagrange-type interpolation on an invariance condition under the generalized backward shift operator has also been discussed. Furthermore, the paper establishes the connection between quasi Lagrange-type interpolation, operator of multiplication by the independent variable, and de Branges spaces of vector valued entire functions., Comment: 15 pages

The recruitment process is crucial to an organization's ability to position itself for success, from finding qualified and well-fitting job candidates to impacting its output and culture. Therefore, over the past century, human resources experts and industrial-organizational psychologists have established hiring practices such as attracting candidates with job ads, gauging a candidate's skills with assessments, and using interview questions to assess organizational fit. However, the advent of big data and machine learning has led to a rapid transformation in the traditional recruitment process as many organizations have moved to using artificial intelligence (AI). Given the prevalence of AI-based recruitment, there is growing concern that human biases may carry over to decisions made by these systems, which can amplify the effect through systematic application. Empirical studies have identified prevalent biases in candidate ranking software and chatbot interactions, catalyzing a growing body of research dedicated to AI fairness over the last decade. This paper provides a comprehensive overview of this emerging field by discussing the types of biases encountered in AI-driven recruitment, exploring various fairness metrics and mitigation methods, and examining tools for auditing these systems. We highlight current challenges and outline future directions for developing fair AI recruitment applications, ensuring equitable candidate treatment and enhancing organizational outcomes., Comment: Update: Updated template from previous version for submission to journal

Motivated by the recent experimental results of branching fractions for D_((s))^+ arrows {\eta}^((')) l^+ {\nu}_l decays, which deviate from their SM predictions, we have investigated these decays in W' model and scalar leptoquark model to find possible signatures of new physics (NP) in semileptonic charm decays induced by c arrow(s,d)l {nu}_l transitions. Using recent experimental results of branching fractions for semileptonic D meson decays, new coupling parameters are predicted for the above NP models. Branching fraction, forward-backward asymmetry and lepton polarization asymmetry are studied taking the predicted NP coupling parameters. Results of branching fractions in scalar leptoquark model are found very close to the experimental results and exist around the range 1{sigma} deviation. We have presented a comparative study of the NP models to check their sensitivity on these decays. We anticipate that further research on these decays will significantly support our findings., Comment: 23 pages, It is accepted and is to be published in Chinese Physics C

Light dark matter (DM) with mass around the GeV scale faces weaker bounds from direct detection experiments. If DM couples strongly to a light mediator, it is possible to have observable direct detection rate. However, this also leads to a thermally under-abundant DM relic due to efficient annihilation into light mediators. We propose a novel scenario where a first-order phase transition (FOPT) occurring at MeV scale can restore GeV scale DM relic by changing the mediator mass sharply at the nucleation temperature. The MeV scale FOPT predicts stochastic gravitational waves with nano-Hz frequencies within reach of pulsar timing array (PTA) based experiments like NANOGrav. In addition to enhancing direct detection rate, the light mediator can also give rise to the required DM self-interactions necessary to solve the small scale structure issues of cold dark matter. The existence of light scalar mediator and its mixing with the Higgs keep the scenario verifiable at different particle physics experiments., Comment: 23 pages, 9 captioned figures, matches version accepted for publication in JCAP

The deployment of beyond fifth-generation (5G) infrastructure over disaster-affected regions, temporary hotspot situations (e.g., massive gatherings, etc.), complex terrains (e.g., sea, hills, marshes, etc.) poses numerous challenges for cellular service providers. Recently, unmanned aerial vehicles (UAVs) have emerged as potential candidates to overcome the aforementioned technical issues based on their multi-role capabilities to serve as aerial base stations, mobile relays, and flying wireless access points. As such, the UAVs can act as portable platforms that can be deployed immediately on demand without requiring massive ground infrastructure to support wireless services. This article introduces the integration of UAVs to cloud radio access networks (C-RAN) for beyond 5G applications. The article mainly focuses on the underlying opportunities and challenges to realize the UAV-assisted C-RAN (UC-RAN) architecture in view of three generic application scenarios, i.e., disaster management, hotspots, and complex terrains. A preliminary performance analysis via simulation is further provided for the proposed UC-RAN under hotspot application scenario based on the relevant metrics., Comment: 15 pages, 4 figures, 2 Tables, Submitted for possible publication as a magazine article

Automatic speech recognition (ASR) systems, increasingly prevalent in education, healthcare, employment, and mobile technology, face significant challenges in inclusivity, particularly for the 80 million-strong global community of people who stutter. These systems often fail to accurately interpret speech patterns deviating from typical fluency, leading to critical usability issues and misinterpretations. This study evaluates six leading ASRs, analyzing their performance on both a real-world dataset of speech samples from individuals who stutter and a synthetic dataset derived from the widely-used LibriSpeech benchmark. The synthetic dataset, uniquely designed to incorporate various stuttering events, enables an in-depth analysis of each ASR's handling of disfluent speech. Our comprehensive assessment includes metrics such as word error rate (WER), character error rate (CER), and semantic accuracy of the transcripts. The results reveal a consistent and statistically significant accuracy bias across all ASRs against disfluent speech, manifesting in significant syntactical and semantic inaccuracies in transcriptions. These findings highlight a critical gap in current ASR technologies, underscoring the need for effective bias mitigation strategies. Addressing this bias is imperative not only to improve the technology's usability for people who stutter but also to ensure their equitable and inclusive participation in the rapidly evolving digital landscape., Comment: Accepted to NAACL 2024

A single layer ferromagnetic film magnetized in the plane of an ac current flow, exhibits a characteristic Hall voltage with harmonic and second harmonic components, which is attributed to the presence of spin currents with polarization non-collinear with the magnetization. A set of 30 nm thick permalloy (Py) films used in this study are deposited at an oblique angle with respect to the substrate plane which induces an in-plane easy axis in the magnetization of the initial nucleating layers of the films which is distinct from the overall bulk magnetic properties of the film. This unusual magnetic texture provides a platform for the direct detection of inverse spin Hall effect in Hall bar shaped macroscopic devices at room temperatures which we denote as Anomalous Inverse Spin Hall Effect (AISHE). Control samples fabricated by normal deposition of permalloy with slow rotation of substrate shows significant reduction of the harmonic Hall signal that further substantiates the model. The analysis of the second harmonic Hall signal corroborates the presence of spin-orbit torque arising from the unconventional spin-currents in the single-layer ferromagnets.

General relativity (GR) has proven to be a highly successful theory of gravity since its inception. The theory has thrivingly passed numerous experimental tests, predominantly in weak gravity, low relative speeds, and linear regimes, but also in the strong-field and very low-speed regimes with binary pulsars. Observable gravitational waves (GWs) originate from regions of spacetime where gravity is extremely strong, making them a unique tool for testing GR, in previously inaccessible regions of large curvature, relativistic speeds, and strong gravity. Since their first detection, GWs have been extensively used to test GR, but no deviations have been found so far. Given GR's tremendous success in explaining current astronomical observations and laboratory experiments, accepting any deviation from it requires a very high level of statistical confidence and consistency of the deviation across GW sources. In this paper, we compile a comprehensive list of potential causes that can lead to a false identification of a GR violation in standard tests of GR on data from current and future ground-based GW detectors. These causes include detector noise, signal overlaps, gaps in the data, detector calibration, source model inaccuracy, missing physics in the source and in the underlying environment model, source misidentification, and mismodeling of the astrophysical population. We also provide a rough estimate of when each of these causes will become important for tests of GR for different detector sensitivities. We argue that each of these causes should be thoroughly investigated, quantified, and ruled out before claiming a GR violation in GW observations., Comment: Review article; 1 figure; 1 table; comments welcome

We study the possibility of generating dark matter (DM) and baryon asymmetry of the Universe (BAU) simultaneously in an asymmetric DM framework, which also alleviates the small-scale structure issues of cold DM. While the thermal relic of such self-interacting DM remains under-abundant due to efficient annihilation into light mediators, a nonzero asymmetry in the dark sector can lead to the survival of the required DM in the Universe. The existence of a light mediator leads to the required self-interactions of DM at small scales while keeping DM properties similar to cold DM at large scales. It also ensures that the symmetric DM component annihilates away, leaving the asymmetric part in the spirit of cogenesis. The particle physics implementation is done in canonical seesaw models of light neutrino mass, connecting it to the origin of DM and BAU. In particular, we consider type-I and type-III seesaw origin of neutrino mass for simplicity and minimality of the field content. We show that the desired self-interactions and relic of DM together with BAU while satisfying relevant constraints lead to strict limits on DM mass $\mathcal{O}({\rm GeV}) \lesssim M_{\rm DM} \lesssim460 $ GeV. In spite of being a high-scale seesaw, the models remain verifiable in different experiments, including direct and indirect DM searches as well as colliders., Comment: 20 pages, 18 captioned figures, version accepted for publication in Phys. Rev. D

Fr\'echet Video Distance (FVD), a prominent metric for evaluating video generation models, is known to conflict with human perception occasionally. In this paper, we aim to explore the extent of FVD's bias toward per-frame quality over temporal realism and identify its sources. We first quantify the FVD's sensitivity to the temporal axis by decoupling the frame and motion quality and find that the FVD increases only slightly with large temporal corruption. We then analyze the generated videos and show that via careful sampling from a large set of generated videos that do not contain motions, one can drastically decrease FVD without improving the temporal quality. Both studies suggest FVD's bias towards the quality of individual frames. We further observe that the bias can be attributed to the features extracted from a supervised video classifier trained on the content-biased dataset. We show that FVD with features extracted from the recent large-scale self-supervised video models is less biased toward image quality. Finally, we revisit a few real-world examples to validate our hypothesis., Comment: CVPR 2024. Project webpage: https://content-debiased-fvd.github.io/